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Only shuffle for training in the evals code #27

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Only shuffle for training in the evals code #27

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23e458c
Update video_dataset.py
bryant1410 Feb 22, 2024
6271680
Update data_manager.py
bryant1410 Feb 22, 2024
2311c76
Update image_dataset.py
bryant1410 Feb 22, 2024
392d798
Update video_dataset.py
bryant1410 Feb 22, 2024
5f025e1
Update eval.py
bryant1410 Feb 22, 2024
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1 change: 1 addition & 0 deletions evals/video_classification_frozen/eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -472,6 +472,7 @@ def make_dataloader(
data_loader, _ = init_data(
data=dataset_type,
root_path=root_path,
training=training,
transform=transform,
batch_size=batch_size,
world_size=world_size,
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2 changes: 2 additions & 0 deletions src/datasets/data_manager.py
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Expand Up @@ -63,6 +63,7 @@ def init_data(
persistent_workers=persistent_workers,
copy_data=copy_data,
drop_last=drop_last,
shuffle=training,
subset_file=subset_file)

elif data.lower() == 'videodataset':
Expand All @@ -86,6 +87,7 @@ def init_data(
world_size=world_size,
rank=rank,
drop_last=drop_last,
shuffle=training,
log_dir=log_dir)

return (data_loader, dist_sampler)
4 changes: 3 additions & 1 deletion src/datasets/image_dataset.py
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Expand Up @@ -53,6 +53,7 @@ def make_imagedataset(
copy_data=False,
drop_last=True,
persistent_workers=False,
shuffle=True,
subset_file=None
):
dataset = ImageFolder(
Expand All @@ -64,7 +65,8 @@ def make_imagedataset(
dist_sampler = torch.utils.data.distributed.DistributedSampler(
dataset=dataset,
num_replicas=world_size,
rank=rank)
rank=rank,
shuffle=shuffle)
data_loader = torch.utils.data.DataLoader(
dataset,
collate_fn=collator,
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5 changes: 3 additions & 2 deletions src/datasets/video_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@ def make_videodataset(
num_workers=10,
pin_mem=True,
duration=None,
shuffle=True,
log_dir=None,
):
dataset = VideoDataset(
Expand All @@ -66,13 +67,13 @@ def make_videodataset(
dataset.sample_weights,
num_replicas=world_size,
rank=rank,
shuffle=True)
shuffle=shuffle)
else:
dist_sampler = torch.utils.data.distributed.DistributedSampler(
dataset,
num_replicas=world_size,
rank=rank,
shuffle=True)
shuffle=shuffle)

data_loader = torch.utils.data.DataLoader(
dataset,
Expand Down